Image Segmentation and Processing for Efficient Parking Space Analysis

TL;DR

This paper presents a MATLAB-based image processing method for accurately detecting vacant parking spaces in challenging conditions, reducing costs by avoiding individual sensors and improving detection efficiency.

Contribution

It introduces a novel combination of pre-processing and false contour detection techniques to enhance parking space detection accuracy in complex environments.

Findings

Effective detection of vacant spots despite uneven illumination and overlapping cars.

Reduces system costs by using static images instead of sensors.

Outperforms conventional algorithms in accuracy and reliability.

Abstract

In this paper, we develop a method to detect vacant parking spaces in an environment with unclear segments and contours with the help of MATLAB image processing capabilities. Due to the anomalies present in the parking spaces, such as uneven illumination, distorted slot lines and overlapping of cars. The present-day conventional algorithms have difficulties processing the image for accurate results. The algorithm proposed uses a combination of image pre-processing and false contour detection techniques to improve the detection efficiency. The proposed method also eliminates the need to employ individual sensors to detect a car, instead uses real-time static images to consider a group of slots together, instead of the usual single slot method. This greatly decreases the expenses required to design an efficient parking system. We compare the performance of our algorithm to that of other techniques. These comparisons show that the proposed algorithm can detect the vacancies in the parking spots while ignoring the false data and other distortions.